Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic. One of the most popular methods for manipulating digital images is image splicing, which involves copying a specific area from one image and pasting it into another. Attempts were made to mitigate the effects of image splicing, which continues to be a significant research challenge. This study proposes a new splicing detection model, combining Sonine functions-derived convex-based features and deep features. Two stages make up the proposed method. The first step entails feature extraction, then classification using the “support vector machine” (SVM) to differentiate authentic and spliced images. The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels. The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset “Chinese Academy of Sciences, Institute of Automation” which is a publicly available dataset for forgery classification. The experimental results show that, for image splicing forgery detection, the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy, precision, and recall. Overall, the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations. Finding the regions or locations where image tampering has taken place is limited by the study. Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.